School of Journalism and Media, University of Texas at Austin, Austin, TX, United States.
University of Texas Health Science Center at Houston School of Public Health, Houston, TX, United States.
J Med Internet Res. 2024 Sep 11;26:e55591. doi: 10.2196/55591.
Social media posts that portray vaping in positive social contexts shape people's perceptions and serve to normalize vaping. Despite restrictions on depicting or promoting controlled substances, vape-related content is easily accessible on TikTok. There is a need to understand strategies used in promoting vaping on TikTok, especially among susceptible youth audiences.
This study seeks to comprehensively describe direct (ie, explicit promotional efforts) and indirect (ie, subtler strategies) themes promoting vaping on TikTok using a mixture of computational and qualitative thematic analyses of social media posts. In addition, we aim to describe how these themes might play a role in normalizing vaping behavior on TikTok for youth audiences, thereby informing public health communication and regulatory policies regarding vaping endorsements on TikTok.
We collected 14,002 unique TikTok posts using 50 vape-related hashtags (eg, #vapetok and #boxmod). Using the k-means unsupervised machine learning algorithm, we identified clusters and then categorized posts qualitatively based on themes. Next, we organized all videos from the posts thematically and extracted the visual features of each theme using 3 machine learning-based model architectures: residual network (ResNet) with 50 layers (ResNet50), Visual Geometry Group model with 16 layers, and vision transformer. We chose the best-performing model, ResNet50, to thoroughly analyze the image clustering output. To assess clustering accuracy, we examined 4.01% (441/10,990) of the samples from each video cluster. Finally, we randomly selected 50 videos (5% of the total videos) from each theme, which were qualitatively coded and compared with the machine-derived classification for validation.
We successfully identified 5 major themes from the TikTok posts. Vape product marketing (1160/10,990, 8.28%) reflected direct marketing, while the other 4 themes reflected indirect marketing: TikTok influencer (3775/14,002, 26.96%), general vape (2741/14,002, 19.58%), vape brands (2042/14,002, 14.58%), and vaping cessation (1272/14,002, 9.08%). The ResNet50 model successfully classified clusters based on image features, achieving an average F-score of 0.97, the highest among the 3 models. Qualitative content analyses indicated that vaping was depicted as a normal, routine part of daily life, with TikTok influencers subtly incorporating vaping into popular culture (eg, gaming, skateboarding, and tattooing) and social practices (eg, shopping sprees, driving, and grocery shopping).
The results from both computational and qualitative analyses of text and visual data reveal that vaping is normalized on TikTok. Our identified themes underscore how everyday conversations, promotional content, and the influence of popular figures collectively contribute to depicting vaping as a normal and accepted aspect of daily life on TikTok. Our study provides valuable insights for regulatory policies and public health initiatives aimed at tackling the normalization of vaping on social media platforms.
在正面的社会环境中描绘蒸气的社交媒体帖子塑造了人们的认知,并使蒸气正常化。尽管对描绘或宣传管制药物有限制,但 TikTok 上很容易获取与蒸气相关的内容。需要了解在 TikTok 上推广蒸气的策略,特别是针对易受影响的青年受众。
本研究旨在使用社交媒体帖子的计算和定性主题分析的混合方法,全面描述在 TikTok 上推广蒸气的直接(即明确的推广努力)和间接(即更微妙的策略)主题。此外,我们旨在描述这些主题如何在 TikTok 上为青年观众塑造蒸气行为的正常化,从而为有关 TikTok 上蒸气认可的公共卫生交流和监管政策提供信息。
我们使用 50 个与蒸气相关的标签(例如#vapetok 和#boxmod)收集了 14002 个独特的 TikTok 帖子。使用 k-均值无监督机器学习算法,我们确定了聚类,然后根据主题对帖子进行定性分类。接下来,我们根据主题对所有帖子进行组织,并使用 3 种基于机器学习的模型架构提取每个主题的视觉特征:具有 50 层的残差网络(ResNet)(ResNet50),具有 16 层的视觉几何组模型和视觉转换器。我们选择表现最佳的模型 ResNet50 来彻底分析图像聚类输出。为了评估聚类准确性,我们检查了每个视频群集中的 4.01%(441/10990)的样本。最后,我们从每个主题中随机选择了 50 个视频(总视频的 5%),对其进行定性编码,并与机器生成的分类进行比较以进行验证。
我们从 TikTok 帖子中成功识别出 5 个主要主题。蒸气产品营销(1160/10990,8.28%)反映了直接营销,而其他 4 个主题则反映了间接营销:TikTok 影响者(3775/14002,26.96%),一般蒸气(2741/14002,19.58%),蒸气品牌(2042/14002,14.58%)和蒸气戒断(1272/14002,9.08%)。ResNet50 模型成功地根据图像特征对聚类进行分类,平均 F 分数为 0.97,在 3 种模型中得分最高。定性内容分析表明,蒸气被描绘为日常生活中正常的、常规的一部分,TikTok 影响者将蒸气巧妙地融入流行文化(例如游戏、滑板和纹身)和社会实践(例如购物狂欢、驾驶和杂货店购物)中。
文本和视觉数据的计算和定性分析的结果表明,蒸气在 TikTok 上是正常化的。我们确定的主题强调了日常对话、推广内容和流行人物的影响如何共同将蒸气描绘成 TikTok 日常生活中正常和可接受的一部分。我们的研究为旨在解决社交媒体平台上蒸气正常化问题的监管政策和公共卫生倡议提供了有价值的见解。